There are some previous methods to solve measure of uncertain in unknown environment. The random prior function [1]} is originally used to enhance the performance of Bootstrapped DQN. While training bootstrapped function to fit Q with a posterior probability, random network gives each bootstrapped function a fixed prior. Since the prior probabilities are initialized randomly, it is possible to improve the diversity of bootstrapped functions and better fit the distribution of the posterior probabilities. The measurement method of uncertainty obtained by fitting random prior has been proved to be successful in theory and application [2]}, [3]}. The Deep ensembles [4]} is a commonly used measurement method of uncertainty. Each model in the ensemble is trained based on bootstrap data, so that the predicted variance between models can be utilized as a measure of epistemic uncertainty. The disadvantage of deep ensembles is that it tends to give overconfident estimates of uncertainty. For obtaining uncertainty estimates, dropout [5]} is firstly proposed to model uncertainty in Deep Learning as a practical tool, and it can also be extended to quasi-KL [6]}. However, in this work, we focus on measuring the uncertainty of the environment with Bayesian Neural network (BNN)[7]}, [8]}. BNN is a traditional approach to measure uncertainty, which combines probabilistic modeling with neural networks and is able to output the degree of confidence of prediction results.
